An Improvement of Dissimilarity-Based Classifications Using SIFT Algorithm

  • Evensen E. Masaki
  • Sang-Woon Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6744)

Abstract

In dissimilarity-based classifications (DBCs), classifiers are not based on the feature measurements of individual objects, but rather on a suitable dissimilarity measure among the objects. In this paper, we study a new way of measuring the dissimilarity between two object images using a SIFT (Scale Invariant Feature Transformation) algorithm [5], which transforms image data into scale-invariant coordinates relative to local features based on the statistics of gray values in scale-space. With this method, we find an optimal or nearly optimal matching among differing images in scaling and rotation, which leads us to obtain dissimilarity representation after matching them. Our experimental results, obtained with well-known benchmark databases, demonstrate that the proposed mechanism works well and, compared with the previous approaches, achieves further improved results in terms of classification accuracy.

Keywords

Face Recognition Object Image Dynamic Time Warping Scale Invariant Feature Transformation Benchmark Database 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Evensen E. Masaki
    • 1
  • Sang-Woon Kim
    • 1
  1. 1.Dept. of Computer Science and EngineeringMyongji UniversityYonginSouth Korea

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